Not applicable.
The present invention relates to a system and method for identifying which individual persons admitted to an intensive care unit (ICU) are likely to die prior to hospital discharge, and which persons are likely to recover and survive to discharge.
A very difficult clinical problem facing clinicians is knowing when further treatment is futile and no longer appropriate in a patient who has developed severe complications after surgery and is being treated in an ICU. It is now possible to prolong the process of dying among such patients. This results in unnecessary pain and loss of dignity for the patient, anguish and distress for the patient""s relatives and is dehumanizing for the clinical and nursing staff. It has also tremendous implications in the use of limited health care resources.
The complementary clinical problem facing clinicians, that of knowing when further aggressive treatment is appropriate and has reasonable odds of saving the patient""s life and yielding quality of life and other benefits in addition to survival, is equally difficult. Prognostic criteria based on xe2x80x9cstaticxe2x80x9d analysis of group statistics are of little value in decisions to withhold or withdraw therapy from ICU patients too ill to benefit, since they do not provide adequate information on the features that distinguish non-survivors from survivors.
In 1990, Smedira and co-workers (Smedira 1990) wrote that xe2x80x9clife support is withheld or withdrawn from many patients especially those with critical illness, but the exact number is not known.xe2x80x9d The terms xe2x80x98withholding of life supportxe2x80x99 and xe2x80x98withdrawal of life supportxe2x80x99 refer to the process according to which various medical interventions either are withheld from patients or discontinued with the expectation that the patient will die as a result.
The feeling that a patient admitted to the ICU should not be considered as a xe2x80x98terminal patientxe2x80x99 produces difficulties associated with rationalizing the use of new and costly technologies in a situation where resources are scarce, such as the modern healthcare systems. This has led to the development of new systems for the assessment of severity for use in the ICU. These tools may be used to predict outcome, although their use has ethical and financial implications.
Over the last 15 years, several systems for the assessment of multiorgan failure have been developed, mainly because multiorgan failure represents the main cause of mortality and morbidity among critically ill patients managed in the ICU.
A computer model designed to improve the process of decision-making concerning the continuation or escalation of aggressive ICU interventions, or of abating interventions that are probably futile, must possess the following properties: it must reflect the time-dependent, or xe2x80x9cdynamicxe2x80x9d, pathophysiological process and be able to predict death with high accuracy, early in the clinical course when there is but a scanty timeseries comprised of very few ( less than 10) values of predictive scores or physiologic indices of end-organ function. It is important to emphasize that serial scoring is not purely a reflection of the inherent potential of the patient""s organ systems to recover function sufficient to sustain life; it reflects the ability of an intensive care unit to stabilize the patient or reverse the physiological dysfunction that is present.
Prior art includes Chang""s algorithm (Chang 1988), which used computerized dynamic trend analysis of daily organ failure scores (APACHE II score, corrected for the number and duration of organ failures), noting the rate of change in score relative to that of the previous day and an absolute threshold to predict death. The algorithm was developed by tracking the daily scores of 200 ICU patients until their death or discharge from the ICU. It was subsequently validated on 831 patients. Chang""s approach had poor sensitivity (predicted only 38% of all deaths) with acceptable statistical specificity (no false-positive predictions). More recent trend analysis techniques such as SOFA (xe2x80x98sequential organ failure assessmentxe2x80x99, see Cook 2001, Hutchinson 2000, Pettila 2002, Rosenberg 2002) continue to suffer from inadequate sensitivity.
Other algorithms using serial scoring have had unacceptably high false-positive rates. For example, Atkinson""s study (Atkinson 1994) showed a false-positive diagnosis rate of 4.4%. If used prospectively, this algorithm does have the potential to indicate the futility of continued intensive care but at the high cost of nearly 1 in 20 patients who would survive if intensive care were continued.
Chang""s and others"" approaches were unsatisfactory in terms of excessive reliance on long timeseries (74% of predictions resolvable by trend analysis required data from seven days or more in the ICU, too long to be of significant help in the contemporary situation with its focus on prompt decision-making soon after admission and aggressive discharge-planning). In part, the failure of the prior art can be traced to inappropriate pooling of data from groups of patients with markedly differing mortality rates, including many whose probability of in-hospital death was low or moderate. For this reason and many others, there is a need for a system and method overcoming the deficiencies of the prior art.
Atkinson S, et al. Identification of futility in intensive care. Lancet. 1994 Oct. 29;344(8931):1203-6.
Chang R W, et al. Predicting outcome among intensive care unit patients using computerised trend analysis of daily Apache II scores corrected for organ system failure. Intensive Care Med. 1988;14:558-66.
Chang R W, Bihari D S. Outcome prediction for the individual patient in the ICU. Unfallchirurg. 1994 April;97(4): 199-204.
Christakis N A, Asch D A. Biases in how physicians choose to withdraw life support. Lancet. 1993;342:642-6.
Cook R, et al. Multiple organ dysfunction: baseline and serial component scores. Crit Care Med. 2001 November;29(11):2046-50.
De Queiroz M S, et al. Lyapunov-Based Control of Mechanical Systems. Birkhauser, 2000.
Elger C E. Nonlinear EEG analysis and its potential role in epileptology. Epilepsia. 2000;41 Suppl 3:S34-8.
Freeman R A, Kokotovic P V. Robust Nonlinear Control Design: State-Space and Lyapunov Techniques. Springer Verlag, 1996.
Goldberger A L, West B J. Applications of nonlinear dynamics to clinical cardiology. Ann New York Acad. Sci. 1987;504: 155-212.
Goldberger A L. Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside. Lancet. 1996; 347:1312-4.
Hutchinson C, et al. Sequential organ scoring as a measure of effectiveness of critical care. Anesthesia. 2000 December;55(12):1149-54.
Knaus W A, Wagner D P. Multiple systems organ failure: epidemiology and prognosis. Crit Care Clin. 1989 April;5(2):221-32.
Lakshmikantham V, et al. Vector Lyapunov Functions and Stability Analysis of Nonlinear Systems. Kluwer, 1991.
Pai M A. Power System Stability: Analysis by the Direct Method of Lyapunov. Elsevier, 1981.
Pettila V, et al. Comparison of multiple organ dysfunction scores in the prediction of hospital mortality in the critically ill. Crit Care Med. 2002 August;30(8):1705-11.
Pool R. Is it healthy to be chaotic? Science. 1989;243:604-7.
Rosenberg A L. Recent innovations in intensive care unit risk-prediction models. Curr Opin Crit Care. 2002 August;8(4):321-30.
Smedira N G, et al. Withholding and withdrawal of life support from the critically ill. N Engl J. Med. 1990;322:309-15.
Wagner D P, et al. Daily prognostic estimates for critically ill adults in intensive care units: results from a prospective, multicenter, inception cohort analysis. Crit Care Med. 1994 September;22(9): 1359-72.
Wihstutz L, Arnold L. Lyapunov Exponents. Springer Verlag, 1986.
The present invention is a method and system mitigating the limitations enumerated above and suitable for an improved system and method for predicting mortality risk using a Lyapunov stability classifier.
In one embodiment of the present invention, a method in a computing environment for effecting a statistical assessment of mortality-predictive patterns in longitudinal timeseries data from individual persons admitted to hospital-based intensive care is provided. The method includes the following steps: accessing mortality-predictive serial data received from a plurality of scores; performing spectral analysis; calculating the Lyapunov exponent, and if the Lyapunov exponent is negative, outputting values for the exponent for a plurality of times in the timeseries.
Additional advantages and novel features of the invention will be set forth in part in a description which follows, and in part will become apparent to those skilled in the art upon examination of the following, or may be learned by practice of the invention.